class_docstr: A compact implementation of agents for Atari 100k.
docstr: Resets the agent state by filling it with zeros.
docstr_tok: ["Resets","the","agent","state","by","filling","it","with","zeros","."]
code: def _reset_state(self, n_envs=None):
      """Resets the agent state by filling it with zeros."""
      if n_envs is None:
          self.state = onp.zeros((1, *self.state_shape))
      else:
          self.state = onp.zeros((n_envs, *self.state_shape))
  
imports: import copy
  import functools
  from absl import logging
  from dopamine.jax import networks
  from dopamine.jax.agents.full_rainbow import full_rainbow_agent
  import gin
  import jax
  import jax.numpy
  import numpy
  import tensorflow
label_desc: Use image node_id 4 for calling the Atari100kRainbowAgent obj's underlying member method code with example usage: obj._reset_state(n_envs) without return types